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Proceedings Paper

Hybrid Associative Memories And Metric Data Models
Author(s): Lev Goldfarb; Raj Verma
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Paper Abstract

An approach to the design of associative memories and pattern recognition systems which utilizes efficiently hybrid architectures is illustrated. By associative memory we mean a database organization that supports retrieval by content and not only by name (or address), as is the case with practically all existing database systems. The approach is based on a general, metric, model for pattern recognition which was developed to unify in a single model two basic approaches to pattern recognition-geometric and structural-preserving the advantages of each one. The metric model offers the designer a complete freedom in the choice of both the object representation and the dissimilarity measure, and at the same time provides a single analytical framework for combining several object representations in a very efficient recognition scheme. It is our fervent hope that the paper will attract researchers interested in the development of associative memories or image recognition systems to experiment with various optical dissimilarity measures (between two images) the need for which becomes so acute with the realization of the possibilities offered by the metric model.

Paper Details

Date Published: 22 August 1988
PDF: 15 pages
Proc. SPIE 0938, Digital and Optical Shape Representation and Pattern Recognition, (22 August 1988); doi: 10.1117/12.976606
Show Author Affiliations
Lev Goldfarb, University of New Brunswick and Intelligent Information Systems (United States)
Raj Verma, University of Toronto (Canada)

Published in SPIE Proceedings Vol. 0938:
Digital and Optical Shape Representation and Pattern Recognition
Richard D. Juday, Editor(s)

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